Results of the summarizing process for the arXiv paper: 2311.06783v1

Multi-modality foundation models, such as GPT-4V, have revolutionized low-level visual perception and understanding tasks by effectively responding to a wide range of natural human instructions. However, while these models have shown promising potential in low-level visual tasks, their capabilities are still in the early stages and require further improvement. To enhance these models, we conducted a large-scale subjective experiment that collected a vast number of real human feedback on low-level vision. The experiment involved detailed descriptions of the low-level visual appearance of various images, including factors such as clarity, color, and brightness. Each feedback provided an overall conclusion about the quality of the image and had an average length of 45 words. This resulted in the creation of the Q-Pathway dataset, which consists of 58K detailed human feedbacks on 18,973 images with diverse low-level appearances. To enable foundation models to respond robustly to different types of questions, we designed a conversion process using GPT (Generative Pre-trained Transformer) to transform these feedbacks into diverse-format instruction-response pairs. This resulted in a dataset called Q-Instruct, which includes 200K instruction-response pairs. Experimental results have shown that the Q-Instruct dataset consistently enhances low-level perception and understanding abilities across several foundational models. We believe that our datasets can pave the way for future advancements in general intelligence's ability to perceive and understand low level visual appearance and evaluate visual quality like humans do. For more information about our datasets, model zoo, and demo please visit our website at https://qfuture.github.io/QInstruct/. In addition to this research work, we also provide some additional context related to composition techniques like the rule of thirds, leading lines, and framing. These techniques can further enhance one's understanding of low level visual attributes and improve image quality.

• Multi-modality foundation models, such as GPT-4V, have revolutionized low-level visual perception and understanding tasks
• These models effectively respond to a wide range of natural human instructions
• However, their capabilities are still in the early stages and require further improvement
• A large-scale subjective experiment was conducted to collect real human feedback on low-level vision
• The experiment involved detailed descriptions of the low-level visual appearance of various images, including factors such as clarity, color, and brightness
• The Q-Pathway dataset was created, consisting of 58K detailed human feedbacks on 18,973 images with diverse low-level appearances
• A conversion process using GPT was designed to transform the feedbacks into diverse-format instruction-response pairs, resulting in the Q-Instruct dataset with 200K pairs
• Experimental results have shown that the Q-Instruct dataset consistently enhances low-level perception and understanding abilities across several foundational models
• The datasets can pave the way for future advancements in general intelligence's ability to perceive and understand low level visual appearance and evaluate visual quality like humans do
• Additional context is provided related to composition techniques like the rule of thirds, leading lines, and framing

Summary1. Models like GPT-4V have improved how computers understand pictures. 2. These models can understand different instructions from people. 3. They still need to get better and improve more. 4. People did an experiment to get feedback on how images look. 5. The experiment helped create a dataset that can help computers see and understand images better. Definitions- Multi-modality foundation models: Computer programs that can understand different types of information, like pictures and words. - Revolutionized: Changed in a big way. - Low-level visual perception: How well a computer can see and understand basic details in pictures. - Capabilities: What something is able to do or achieve. - Subjective experiment: A test where people give their opinions or thoughts about something. - Feedback: Information or comments given by people to help improve something. - Dataset: A collection of information or data used for research or study purposes. - Appearance: How something looks, including factors like clarity (how clear it is), color, and brightness (how light or dark it is). - Conversion process: Changing one form of information into another form. - Instruction-response pairs: Sets of instructions given by people and the corresponding responses from the computer program. - Enhances: Makes better or improves something. - Foundational models: Basic computer programs that are used as building blocks for more advanced ones.

The Revolution of Low-Level Visual Perception and Understanding Tasks

In recent years, multi-modality foundation models such as GPT-4V have revolutionized low-level visual perception and understanding tasks. These models are capable of responding to a wide range of natural human instructions, making them highly useful for various applications. However, while these models have shown promising potential in low-level visual tasks, their capabilities are still in the early stages and require further improvement.

Enhancing Foundation Models with Real Human Feedback

To enhance these models, researchers conducted a large-scale subjective experiment that collected real human feedback on low-level vision. The experiment involved detailed descriptions of the low-level visual appearance of various images, including factors such as clarity, color, and brightness. Each feedback provided an overall conclusion about the quality of the image and had an average length of 45 words. This resulted in the creation of two datasets: Q-Pathway and Q-Instruct.

Q - Pathway Dataset

The Q - Pathway dataset consists of 58K detailed human feedbacks on 18,973 images with diverse low level appearances. It is composed mainly from subjective evaluations regarding image quality which can be used to train or evaluate machine learning algorithms for automated image assessment tasks like photo editing or retouching applications.

Q - Instruct Dataset

To enable foundation models to respond robustly to different types of questions, researchers designed a conversion process using GPT (Generative Pre - trained Transformer) to transform these feedbacks into diverse - format instruction - response pairs. This resulted in a dataset called Q - Instruct which includes 200K instruction - response pairs that can be used to improve model performance when it comes to understanding natural language instructions related to low level visuals such as brightness or clarity .

Experimental Results

Experimental results have shown that the Q – Instruct dataset consistently enhances low – level perception and understanding abilities across several foundational models when compared against baseline results without using this data set . This suggests that our datasets can pave the way for future advancements in general intelligence's ability to perceive and understand low level visual appearance , evaluate visual quality like humans do , as well as provide additional context related to composition techniques like rule of thirds , leading lines , framing etc . For more information about our datasets , model zoo , demo please visit our website at https ://qfuture . github . io /QInstruct/ .